If it don't fit, don't force it

In this article, I discussed univariate probability modeling
techniques, including fitting data to a theoretical probability
distribution and using the fitted theoretical probability distribution
to assign probabilities to various outcomes. I provided a sample model
(of soccer goals) to demonstrate the efficacy of this basic form of
probability modeling and served up a tool to help construct probability
models, a Probability Distributions Library. And you started building
the foundation for understanding and developing more complex forms of
probability models.

In conclusion, the following are some random variables for which you might want to construct probability models as practice:

The time interval between customer orders

The number of customer orders in a given week

The number of people purchasing a particular product in a given week

The time interval between purchases of a particular product

The number of visitors to your Web site at any given minute, hour, or day

The Exponential and Poisson distributions included in the PHPMath_ProbabilityDistribution package are particularly suitable for investigating these random variables further.